When Visual Diversity Is Not Supervision Diversity: Diagnosing Localization-Label Collapse and Persistent Synthetic-to-Real Failure in Construction Object Detection

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Generative synthetic imagery can broaden visual coverage while still providing invalid or incomplete supervision to an object detector. This study revisits a failed synthetic-data pipeline for pile detection and tests whether severe localization-label collapse was sufficient to explain its synthetic-to-real transfer failure. A provenance audit identified a clean corpus of 3200 FLUX.2 [klein] 9B-generated images whose detector labels all used the same normalized box (cx = 0.500, cy = 0.500, w = 0.315, h = 0.525). A balanced 320-image subset was frozen before manual correction and reannotated under a visual-identifiability criterion, yielding 1154 pile instances. The fixed template matched any corrected pile at IoU ≥ 0.50 in only 40/320 images (12.5%) and, by supplying one box per image, necessarily omitted at least 834/1154 corrected instances (72.3%). Four matched YOLOv8n supervision regimes were then compared across three seeds: fixed-template (F0), geometry-matched but image-permuted (P1), corrected-single (C1), and corrected-complete (CAll). C1 and CAll fitted their own synthetic supervision almost perfectly (mAP50 = 0.9949 and 0.9890, respectively), yet real-domain transfer did not recover: on test56, CAll achieved mAP50 = 0.0008 versus 0.0492 for F0; on the secondary 52-image set, CAll achieved 0.0019 versus 0.0237. A small-real-data reference trained on only 19 real images (66 instances) achieved 0.0563 and 0.0298 mAP50 on the same two real sets. A Faster R-CNN control reproduced the absence of corrected-label recovery on the primary real set while fitting CAll in-domain (AP50 = 0.9737). Thus, localization-label collapse was genuine and severe, but correcting it was not sufficient to restore transfer. Annotation integrity is necessary but not sufficient; residual synthetic-to-real mismatch must be evaluated independently of label geometry.